arXiv:2605.21553v1 Announce Type: new Abstract: Tokens are becoming the basic units through which foundation models represent and process information for understanding and inference. However, traditional wireless communication, centered on bit-level fidelity, faces a mismatch between what is transmitted reliably and what downstream models actually consume. This mismatch calls for a communication design that directly accounts for token-level task relevance and downstream model requirements, rather than treating all transmitted bits as equally important. In this paper, we propose TONIC, a token-
The proliferation of foundation models and token-based information processing is highlighting the inefficiency of traditional bit-centric wireless communication, necessitating a new architectural approach.
This development proposes a fundamental shift in how wireless communication is designed and optimized, directly impacting the performance and efficiency of AI-driven systems operating over networks.
Wireless communication will move from optimizing for bit-level fidelity to token-level semantic relevance, creating new design paradigms for hardware and software in connected AI systems.
- · Wireless equipment manufacturers
- · AI model developers
- · Telecommunications companies
- · Edge computing providers
- · Traditional communication protocols
- · Systems optimized solely for bit efficiency
Enhanced efficiency and reliability of data transfer for AI models across wireless networks.
Acceleration of distributed AI and edge computing by reducing communication bottlenecks.
New competitive landscape for wireless technology, favoring those who integrate AI-centric design from the ground up.
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Read at arXiv cs.LG